Epistasis Blog

From the Computational Genetics Laboratory at the University of Pennsylvania (www.epistasis.org)

Monday, October 27, 2008

Genome-Wide Association Studies (GWAS)

A special issue of Human Molecular Genetics that focuses on GWAS was published this month. These papers very much represent the mainstream view of the field and I didn't see anything new that I would recommend reading. When will an editor present a more realistic and complete snapshot of the field and where it is going? Why is there not a paper on complex adaptive systems and their impact on GWAS? Do we not all believe that human health is a complex system?

Tuesday, October 21, 2008

Surviving a Tough Funding Climate - Tips for Junior Faculty

I just returned from a new faculty orientation hosted by our Office of Sposored Projects where I spoke about my experience working with the Dartmouth system. The following are some ideas I shared with the junior faculty in attendence to improve their chances of getting funded. As background reading, see the letter on page 189 in the Oct. 10 Science.

1) Be Flexible. I think the old mantra of keeping your science as focused as possible is outdated in the era of collaborative research. Don't be afraid to go outside your comfort zone in the search for funding opportunities. Look at every RFA and think about how you might fit. Find collaborators in areas you know nothing about and go after the resources that are available. This has worked well for me and will allow you to survive in tough economic times.

2) Write More Grants. The letter in Science I mentioned above states that the total number of NIH R01 submissions has actually decreased. There were 13,659 R01s reviewed in 2006 but only 12,021 reviewed in 2007. This seems odd to me. I have been writing more grants than ever for a wide variety of different funding opportunities. Submit an R01 EVERY cycle and diversify your areas of interest as indicated in point 1 above.

3) Pre-Review Your Grants. Finish the science part of your grant three weeks before it is due and send it out for external peer-review. My previous department would pay a $1000 honorarium for an expert in the field to provide an NIH-style review of a grant that was ready for submission. Have your former mentors or other experts you know read and comment on your grant with enough time for you to make changes before submission. Having a fresh eye look at a grant can make a big difference.

4) Pilot Funding. Identify the sources of pilot funding at your institution and apply for as many as you can. Preliminary data and publications are very important for R01s. Sniff out as many pilot funding opportunities as you can.

5) Clean and Clear. Make your grants as easy to read as possible. Use clear consistent headings with wide margins and plenty of space between paragraphs. I even wrote my last funded R01 in 2-column format and made it look exactly like a Nature Reviews article. Reviewers are very comfortable reading published papers. Something to keep in mind is that NIH is moving to 8-page and 12-page R01 formats so you will need to learn to say the important things in very little space while keeping it easy to read and clear.

6) Multiple PIs. Don't be afraid to make use of the new multiple PI format for your next collaborative grant. When someone asks you to be a collaborator on their NIH R01 think about your role in the proposed work. If you are playing a major role (e.g. 10% effort or more) you should ask to be added as a PI. That way, when the grant gets funded you get credit for having an R01 and the original PI has a much more invested collaborator. A win-win for everyone.

Gene-gene interaction plays an important role in association studies for complex diseases. There have been different approaches to incorporating gene-gene interactions in candidate gene or genome-wide association studies, especially for those genes with no marginal effects but with interaction effects. However, there is no general agreement on how interaction should be tested and how main effects and interaction effects act on a significance signal. In this paper, we propose a test of the null hypothesis of no association in terms of interaction effects for two unlinked loci, which is a 4 degrees of freedom (df) chi-square for two SNPs. The test, derived by contrasting inter-locus disequilibrium measures between cases and controls, can be viewed as the interaction component of the total Pearson chi-square. The remaining portion of the total chi-square can also be used for association analysis, which emphasizes main effects. Simulation studies show that in most situations our interaction test is similar in power to the test based on a logistic regression model but has more power when the genes have no marginal effects. Results also show that single-locus marginal tests can lose much power if interaction effects dominate main effects. For some specific genetic models, the interaction test may be further partitioned into four 1-df chi-squares for individual interaction effect. The interaction pattern can best be demonstrated by the 1-df chi-square components. Simulation results show that there is substantial power gain if interaction patterns are properly incorporated in association analysis.

Friday, October 17, 2008

New Epistasis Essay

Dr. Patrick Phillips has published a very nice new review/essay on epistasis. Is epistasis purely a genetic phenomena as early definitions suggest? Or does epistasis exist in the absence genetic variation? Would Bateson's definition of epistasis have been different had he known about system biology? These are questions we think about. A must read.

Phillips PC. Epistasis - the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet. 2008 [PubMed]

Epistasis, or interactions between genes, has long been recognized as fundamentally important to understanding the structure and function of genetic pathways and the evolutionary dynamics of complex genetic systems. With the advent of high-throughput functional genomics and the emergence of systems approaches to biology, as well as a new-found ability to pursue the genetic basis of evolution down to specific molecular changes, there is a renewed appreciation both for the importance of studying gene interactions and for addressing these questions in a unified, quantitative manner.

Public health research and practice have been guided by a cognitive, rational paradigm where inputs produce linear, predictable changes in outputs. However, the conceptual and statistical assumptions underlying this paradigm may be flawed. In particular, this perspective does not adequately account for nonlinear and quantum influences on human behavior. We propose that health behavior change is better understood through the lens of chaos theory and complex adaptive systems. Key relevant principles include that behavior change (1) is often a quantum event; (2) can resemble a chaotic process that is sensitive to initial conditions, highly variable, and difficult to predict; and (3) occurs within a complex adaptive system with multiple components, where results are often greater than the sum of their parts.

Wednesday, October 15, 2008

MDR software license agreement

Our MDR software is released to the public as open-source under the GNU General Public License (GPL). If you use any of the MDR code in your own software you are required by the GPL to release your software under the GPL. It was brought to my attention recently that the GMDR software I mentioned in my Oct. 9, 2008 post was released under the Academic Free License (AFL). This is not allowed since GMDR makes heavy use of our MDR source code. Please make note of this.

The analysis of gene interactions and epistatic patterns of susceptibility is especially important for investigating complex diseases such as cancer characterized by the joint action of several genes. This work is motivated by a case-control study of bladder cancer, aimed at evaluating the role of both genetic and environmental factors in bladder carcinogenesis. In particular, the analysis of the inflammation pathway is of interest, for which information on a total of 282 SNPs in 108 genes involved in the inflammatory response is available. Detecting and interpreting interactions with such a large number of polymorphisms is a great challenge from both the statistical and the computational perspectives. In this paper we propose a two-stage strategy for identifying relevant interactions: (1) the use of a synergy measure among interacting genes and (2) the use of the model-based multifactor dimensionality reduction method (MB-MDR), a model-based version of the MDR method, which allows adjustment for confounders.

Widespread multifactor interactions present a significant challenge in determining risk factors of complex diseases. Several combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, have emerged as a promising tool for better detecting gene-gene (G x G) and gene-environment (G x E) interactions. We recently developed a general combinatorial approach, namely the generalized multifactor dimensionality reduction (GMDR) method, which can entertain both qualitative and quantitative phenotypes and allows for both discrete and continuous covariates to detect G x G and G x E interactions in a sample of unrelated individuals. In this article, we report the development of an algorithm that can be used to study G x G and G x E interactions for family-based designs, called pedigree-based GMDR (PGMDR). Compared to the available method, our proposed method has several major improvements, including allowing for covariate adjustments and being applicable to arbitrary phenotypes, arbitrary pedigree structures, and arbitrary patterns of missing marker genotypes. Our Monte Carlo simulations provide evidence that the PGMDR method is superior in performance to identify epistatic loci compared to the MDR-pedigree disequilibrium test (PDT). Finally, we applied our proposed approach to a genetic data set on tobacco dependence and found a significant interaction between two taste receptor genes (i.e., TAS2R16 and TAS2R38) in affecting nicotine dependence.

About Me

Edward Rose Professor of Informatics,
Director of the Institute for Biomedical Informatics, Director of the Division of Informatics in the Department of Biostatistics and Epidemiology,
Senior Associate Dean for Informatics,
The Perelman School of Medicine,
University of Pennsylvania